Medication non-adherence is a multifaceted issue, which brings major health risks and healthcare costs with it. Decennia of research have led to the identification of a wide range of determinants for non-adherence. Existing technology-based solutions (e.g., alarm systems) as well as more traditional solutions (e.g., weekly pill box, information leaflets) to promote adherence often address a subset of these determinants using a one-size-fits-all approach. As such, these solutions fail to acknowledge the individual differences between patients. Face to face meetings between the patient and the medical professional allow for a more personalized approach in which the medical professional can assess the needs, interests and situation of the patient and apply a tailored intervention (e.g., health promotion information) that addresses the knowledge and motivation deficits of the patient.
While these types of interventions tailored to the individual have shown to be considerably more effective than generic solutions, they still establish only a moderate effect on adherence. One reason for this could be that the tailoring is limited to the intervention content (e.g., the type of information that is provided), but the communication and information in the intervention is still delivered in a one-size-fits-all format. An enormous amount of research underscores the major differences between individuals in cognitive style (also referred to as “profile” hereinafter), i.e., the way that individuals think, perceive and process information. Matching the information delivery style of an adherence intervention to a person's cognitive style is expected to improve the effect of these solutions. This matching would require that (1) the patient's cognitive style will be assessed and (2) the information/communication delivery style is adapted accordingly.
The assessment of cognitive style poses a major challenge to the medical care professional, due to the fact that a person's cognitive style is not easily identifiable. Moreover, when known, it requires a specific type of knowledge about cognitive style to be able to adapt the intervention effectively.
Traditionally, cognitive style is assessed with the use of questionnaires (e.g., the Myers-Briggs Type Indicator (MBTI) is generally known and widely used instrument for assessing cognitive style). However, using questionnaires in the clinical context would be obtrusive to the patient and disruptive for the clinical workflow. It is known that certain dimensions of cognitive style can also be assessed using the analysis of natural language. This method is very time-consuming when it needs to be done manually by experts.
As for the assessment of cognitive style, the recommendations to tailor interventions/communication to the cognitive style are often unknown to the medical care provider and are quite time consuming to discover. Further, the known methods for eliciting cognitive style impose a burden to the patient, entailing the filling out of extensive questionnaires. Also, assessment by the care provider requires a specific expertise and a considerable amount of time. Moreover, care providers often lack the specific expertise to effectively adapt their style to different types of cognitive styles. Altogether, this leads to a missed opportunity to tailor an intervention to the cognitive style of the specific patient under consideration.
US 2010/075289 A1 discloses a method and system for automated customization of original content for one or more users. One implementation involves obtaining behavior information for a user, profiling the user based on the user behavior information, determining a preferred learning style for the user based on the user profiling, and customizing the original content based on the preferred learning style for the user. Profiling the user may involve analyzing the user behavior information using one or more profiling patterns for profiling the user to determine scores for different behavior categories for the user. Customizing the original content may involve determining a preferred learning style for the user based on the user profiling, and further includes selecting a customization scheme from a scheme repository, based on said scores for different behavior categories for the user, and applying the selected customization scheme to the original content to generate customized content for the user.